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Student Reactions to AI-Replicant Professor in an Econ101 Teaching Video

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  • Rosa-García, Alfonso

Abstract

This study explores student responses to AI-generated educational content, specifically a teaching video delivered by an AI-replicant of their professor. Utilizing ChatGPT-4 for scripting and Heygen technology for avatar creation, the research investigates whether students' awareness of the AI's involvement influences their perception of the content's utility. With 97 participants from first-year economics and business programs, the findings reveal a significant difference in valuation between students informed of the AI origin and those who were not, with the former group valuing the content less. This indicates a bias against AI-generated materials based on their origin. The paper discusses the implications of these findings for the adoption of AI in educational settings, highlighting the necessity of addressing student biases and ethical considerations in the deployment of AI-generated educational materials. This research contributes to the ongoing debate on the integration of AI tools in education and their potential to enhance learning experiences.

Suggested Citation

  • Rosa-García, Alfonso, 2024. "Student Reactions to AI-Replicant Professor in an Econ101 Teaching Video," MPRA Paper 120135, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:120135
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    References listed on IDEAS

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    1. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    2. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2025. "Generative AI at Work," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 140(2), pages 889-942.
    3. Gary Charness & Brian Jabarian & John A. List, 2023. "Generation Next: Experimentation with AI," NBER Working Papers 31679, National Bureau of Economic Research, Inc.
    4. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    5. Farjam, Mike & Kirchkamp, Oliver, 2018. "Bubbles in hybrid markets: How expectations about algorithmic trading affect human trading," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 248-269.
    6. Jean-François Bonnefon & Fatimah Ishowo-Oloko & Zakariyah Soroye & Jacob W. Crandall & Iyad Rahwan & Tahal Rahwan, 2019. "Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation," Post-Print hal-04121730, HAL.
    7. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
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    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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